prof.dr. HC (Hendriek) Boshuizen

Following a PhD-project in occupational epidemiology, I carried out a wide range of research projects in the field of Public Health, including research on public health indicators (Health expectancy), socio-economic and geographic differentials in health, disability, hypertension and several other topics.

After switching to statistics, I participated in a large diversity of research projects, both as an advisor and by developing methods for statistical analysis. Projects including the EPIC study (European Prospective Investigation into Cancer and Nutrition), infectious disease epidemiology and food safety. I applied and further developed methods for handling missing data, dealing with measurement error (including regression calibration) and meta-analysis. My main focus is on methods for integrating knowledge from different information sources, as in the context of chronic disease modelling. I was project leader of the RIVM chronic disease model from 2005 to 2008, and work package leader in the DYNAMO-HIA project, where I led the development of the DYNAMO-HIA software. DYNAMO-HIA is free-to-use software, available at www.dynamo-hia.eu, which calculates future public health impact of changing population exposure to risk factors. I further led the DEDIPOP project (2015-2018), aimed at constructing a synthetic population of the Netherlands for use in policy support, and currently lead the RIVM-AMALGAM project, aimed at capacity building on machine learning and on statistical methods for microbiome data. The latter topic is also the focus of my work at Biometris (Wageningen Research). Furthermore I am leading the WP on Integrative Data Analysis and Modelling in the EU-funded project Equal-Life.

Main current research interests Methodological aspects of integration knowledge from different sources; Statistical methods for analysis of Microbiome data; Machine Learning; Missing data; Measurement error in nutritional epidemiology; Simulation-models of Public Health; Methodology of building Digital Twins for Life Science applications.